Bandwidth allocation in cooperative wireless networks: Buffer load analysis and fairness evaluation

In modern cooperative wireless networks, the resource allocation is an issue of major significance. The cooperation of source and relay nodes in wireless networks towards improved performance and robustness requires the application of an efficient bandwidth sharing policy. Moreover, user requirements for multimedia content over wireless links necessitate the support of advanced Quality of Service (QoS) features. In this paper, a novel bandwidth allocation technique for cooperative wireless networks is proposed, which is able to satisfy the increased QoS requirements of network users taking into account both traffic priority and packet buffer load. The performance of the proposed scheme is examined by analyzing the impact of buffer load on bandwidth allocation. Moreover, fairness performance in resource sharing is also studied. The results obtained for the cooperative network scenario employed, are validated by simulations. Evidently, the improved performance achieved by the proposed technique indicates that it can be employed for efficient traffic differentiation. The flexible design architecture of the proposed technique indicates its capability to be integrated into Medium Access Control (MAC) protocols for cooperative wireless networks.     

The effect of Laplacian filter in adaptive unsharp masking for infrared image enhancement

Image processing, in particular image enhancement techniques have been the focal point of considerable research activity in the last decade. With the aid of an existing image enhancement technique, adaptive unsharp masking (AUM), we propose a novel kernel to be used in AUM filtering in order to enhance discontinuities which occur on the edges of targets of interest in infrared (IR) images. The proposed method uses an adaptive filter approach where an objective function is minimized by using descent algorithms. The output IR image has better sharpness and contrast adjustment for the detection of targets in terms of objective quality metrics. Hence, the proposed method ensures that the edges of the targets in IR images are sharper and that the quality of contrast adjustment has its optimum level in terms of peak signal-to-noise ratios.     

The economics of social computing: Some preliminary findings on healthcare organizations

Large-scale organizations have used social computing platforms for various purposes. This research focuses on how hospitals utilize these platforms to attract potential customers (which represents the “extensivity” of a social computing platform) and generate interests in specific topics (which represents the “intensivity” of a platform). Specifically, we examine the effects of size of a hospital (or “size”) and the time that the social computing platform has been in existence (or “time”) on extensivity and intensivity. Our findings show that time is a significant variable on both dimensions; whereas size affects intensivity under certain conditions. We discuss the implications of these findings, and set the stage for future research.     

Network robustness and irreversibility of information diffusion in Complex networks

Complex networks are characterized based on a newly proposed parameter, “degree of diffusion α”. It defines the ratio of information adopters to non-adopters within a diffusion process over consecutive penetration depths. Furthermore, the perfectness of a social network is evaluated by exploring different variations of α such as the reverse diffusion (α_reverse) and the random-kill-diffusion (RKD) processes. The analysis of α_reverse and RKD processes shows information diffusion irreversibility in small-world and scale-free but not in random networks. It also shows that random networks are more stable toward attacks, resulting a complete information diffusion process over the entire network. Finally, a real Complex network example, represented as a “virtual friendship network” was analyzed and found to share properties of both random and small-world networks. Therefore, it is characterized to be somewhere between random and small-world network models or in other words, it is a randomized small-world network model.     

Green polymer chemistry: Precision synthesis of novel multifunctional poly(ethylene glycol)s using enzymatic catalysis

This paper gives an overview about enzyme catalysis, and reports the precision synthesis of multifunctional poly(ethylene glycol)s using this green chemistry approach. Specifically, vinyl acrylate was transesterified with tetraethylene glycol (TEG) and a PEG with DP_n = 23, and then (HO)₂–TEG–(OH)₂ and (HO)₂–PEG–(OH)₂ were synthesized by the Michael addition of diethanolamine to the acrylate double bonds. These structures will serve as the core of novel dendrimers designed for drug delivery applications.     

Low-temperature wet chemical precipitation of YVO4:Bi,Eu nanophosphors via citrate precursors

YVO₄:Bi³⁺,Eu³⁺ nanophosphors are prepared by low-temperature wet chemical precipitations in the presence of citrate ions. Transparently dispersed YVO₄:Bi³⁺,Eu³⁺ nanophosphors crystallize from amorphous gel precursors through a gel-sol process. The homogeneity of Bi³⁺ doping process as well as the photoluminescence property are improved by using the homogeneous Bi³⁺ solution as a Bi³⁺ source instead of Bi³⁺ powder. YVO₄:Bi³⁺,Eu³⁺ nanophosphor under the irradiation of near-UV light shows peculiar photobleaching behavior through the reduction from V⁵⁺ to V⁴⁺, but this photobleach is suppressed by subsequent hydrothermal treatment. The authors also discuss the advantage of transparency of YVO₄:Bi³⁺,Eu³⁺ nanophosphor in terms of wavelength conversion as compared to micron-sized phosphor.     

Rapid development of scalable scientific software using a process oriented approach

Scientific applications are often not written with multiprocessing, cluster computing or grid computing in mind. This paper suggests using Python and PyCSP to structure scientific software through Communicating Sequential Processes. Three scientific applications are used to demonstrate the features of PyCSP and how networks of processes may easily be mapped into a visual representation for better understanding of the process workflow. We show that for many sequential solutions, the difficulty in implementing a parallel application is removed. The use of standard multi-threading mechanisms such as locks, conditions and monitors is completely hidden in the PyCSP library. We show the three scientific applications: kNN, stochastic minimum search and McStas to scale well on multi-processing, cluster computing and grid computing platforms using PyCSP.     

Target training with soft computing tools

The personal capabilities and intentions of employees indicate their performance within their organization. It is important for the organization to capture this kind of tacit knowledge since the workforce are the true experts in perceiving the organization's current reality and evaluating which assets require development – including themselves as knowledge assets. The collective inner voice of the workforce helps the organization's management to steer the company and its assets in a sustainable direction.This article presents how the collective inner voice of the workforce can be captured and how it can be used for the benefit of the organization and its employees. The objective is to support individuals’ personal aspirations, as well as to save the money, time and resources that an organization spends on personnel training.The focus of this article is on demonstrating a possible soft-computing method used for competency simulation. The process starts with a linguistic self-evaluation conducted by employees, where individuals’ own perception of current and target competence levels is captured. The self-evaluation is conducted with the help of fuzzy logic. Clusters are formed from the result dataset using an unsupervised neural network clustering method: self-organizing maps. A demonstrator tool is then used to perform a “what-if” type of analysis/simulation on the clusters in the results. With the demonstrator tool, employees can roughly test the impact of alternative training scenarios for themselves. For individuals this may open up new directions for self-development, and for organizations this may allow the efficient use of training resources. We tested the approach with a dataset from a real human resource development project among nuclear power plant operators.The case study reveals the potential of soft-computing based collective competency simulation as one part of personnel development projects in the future. Yet the techniques and the demonstrator tool used in this experiment are far from being products that employees could easily use as part of their training project. Possible benefits of the proposed approach are demonstrated in this article.     

Discrete dipole approximation simulations on GPUs using OpenCL—Application on cloud ice particles

The global distribution and climatology of ice clouds are among the main uncertainties in climate modeling and prediction. In order to retrieve ice cloud properties from remote sensing measurements, the scattering properties of all cloud ice particle types must be known. The discrete dipole approximation (DDA) simulates scattering of radiation by arbitrarily shaped particles and is thus suitable for cloud ice crystals. The DDA models the particle as a collection of equal dipoles on a lattice, and is computationally much more expensive than approximations restricted to more regularly shaped particles. On a single computer the calculation for an ice particle of a specific size, for a given scattering plane at one specific wavelength can take several days. We have ported the core routines of the scattering suite “ADDA” to the open computing language (OpenCL), a framework for programming parallel devices like PC graphics cards (graphics processing units, GPUs) or multi-core CPUs. In a typical case we can achieve a speed-up on a GPU as compared to a CPU by a factor of 5 in double precision and a factor of 15 in single precision. Spreading the work load over multiple GPUs will allow calculating the scattering properties even of large cloud ice particles.